To extract structured fields from invoices with an AI API, you typically use one of these approaches:

1) Use a document understanding API

Best for production invoice extraction.

These APIs are built to detect layout and pull fields like:

• invoice number
• date
• vendor name
• bill-to / ship-to
• line items
• subtotal, tax, total
• currency

Examples:

• Google Document AI
• AWS Textract
• Azure Form Recognizer / Document Intelligence
• Mindee
• Veryfi

Typical flow

1. Upload invoice PDF/image
2. Send it to the API
3. Get back JSON with extracted fields
4. Validate/normalize the values in your app

Example output

{
  "invoice_number": "INV-1024",
  "invoice_date": "2026-01-12",
  "vendor_name": "Acme Supplies Inc.",
  "total": 145.67,
  "currency": "USD",
  "line_items": [
    {"description": "Paper", "quantity": 2, "unit_price": 12.50, "amount": 25.00}
  ]
}

2) Use a general LLM with structured output

Best if invoices vary a lot or you already use an LLM platform.

Workflow

1. Convert invoice to text or OCR it first
2. Prompt the model to extract fields
3. Require JSON output using a schema
4. Validate results

Example prompt

Extract the following invoice fields from the text below and return valid JSON matching this schema: invoice_number, invoice_date, vendor_name, bill_to, subtotal, tax, total, currency, line_items[].

Example schema

{
  "type": "object",
  "properties": {
    "invoice_number": {"type": "string"},
    "invoice_date": {"type": "string"},
    "vendor_name": {"type": "string"},
    "total": {"type": "number"}
  },
  "required": ["invoice_number", "invoice_date", "vendor_name", "total"]
}

This is especially useful when:

• invoices are semi-structured
• you need custom fields
• you want one API for many document types

3) Hybrid: OCR + LLM post-processing

Very common in real systems.

Why?

• OCR handles text extraction from scans/photos
• LLM cleans up and maps fields into your schema
• You can add business rules after extraction

Example pipeline

• OCR invoice image → text
• LLM extracts JSON
• Validate totals, dates, currency
• Flag low-confidence invoices for human review

---

Recommended architecture

For most applications:

1. OCR/document AI for text extraction
2. LLM for normalization and schema mapping
3. Validation layer to check:
   • totals = subtotal + tax
   • dates parse correctly
   • currency is valid
   • required fields are present
4. Human review for low-confidence cases

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Minimal API example

If you're using an LLM API with structured output, the idea looks like this:

schema = {
  "type": "object",
  "properties": {
    "invoice_number": {"type": "string"},
    "invoice_date": {"type": "string"},
    "vendor_name": {"type": "string"},
    "total": {"type": "number"}
  },
  "required": ["invoice_number", "invoice_date", "vendor_name", "total"]
}

Then send invoice text along with instructions to return JSON matching the schema.

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Tips for better extraction

• Use high-quality scans or PDFs
• Run OCR before the LLM if the source is an image
• Ask for strict JSON
• Include examples in the prompt
• Normalize dates/currency after extraction
• Use confidence scores if your API provides them
• Build fallback rules for missing fields

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If you want accuracy on invoices specifically

Choose a document AI tool if:

• you need line items
• invoices are scanned and messy
• you care about consistent extraction at scale

Choose an LLM if:

• you need flexibility
• invoice formats vary widely
• you want custom logic or extra fields

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If you want, I can show you:

1. a Python example using an invoice OCR/API, or
2. a prompt + JSON schema for invoice extraction with an LLM.